Graph Enhanced Reinforcement Learning for Effective Group Formation in Collaborative Problem Solving

arXiv:2403.10006v1 Announce Type: cross
Abstract: This study addresses the challenge of forming effective groups in collaborative problem-solving environments. Recognizing the complexity of human interactions and the necessity for efficient collaboration, we propose a novel approach leveraging graph theory and reinforcement learning. Our methodology involves constructing a graph from a dataset where nodes represent participants, and edges signify the interactions between them. We conceptualize each participant as an agent within a reinforcement learning framework, aiming to learn an optimal graph structure …

abstract arxiv challenge collaboration collaborative complexity cs.cy cs.hc cs.lg cs.si environments graph human human interactions interactions methodology novel problem-solving reinforcement reinforcement learning study theory type